Baselines. We implement several deep neural network baselines for geometry solvers to compare them with our method. By default, these baselines formalize the geometry problem solving task as a classification problem, fed by the text embedding from a sequence encoder and the diagram representation from a visual encoder.
Q-only only encodes the problem text in the natural language by a bi-directional Gated Recurrent Unit (Bi-GRU) encoder [1]}.
I-only only encodes the problem diagram by a ResNet-50 encoder [2]} as the input.
Q+I uses Bi-GRU and ResNet-50 to encode the text and diagram, respectively.
RelNet [3]} is implemented for embedding the problem text because it is a strong method for modeling entities and relations.
FiLM [4]} is compared as it achieves effective visual reasoning for answering questions about abstract images.
FiLM-BERT uses the BERT encoder [5]} instead of the GRU encoder, and FiLM-BART uses the recently proposed BART encoder [6]}.
